A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

Abstract

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient.

Methods and materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130).

Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, and gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability (www.predictcancer.org). The data set can be downloaded at https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048.

Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.

Fig. 1

Predictors in the final multivariable model, using transformed variables to simplify the model. No regression coefficients are estimated for the treatment cohorts as overall survival is estimated by the Kaplan-Meier method for the strata (treatment cohorts). EQD₂ = equivalent dose in 2-Gy fractions; GTV = gross tumor volume; OTT = overall treatment time; WHO-PS = World Health Organization performance status.

Fig. 2

Kaplan-Meier curves for overall survival of risk groups, based on the predicted probability for the external validation cohorts from (A) Y (n=174) and (B) Z (n=130). RT = radiation therapy.

Fig. 3

Nomogram for prediction of 24-month overall survival. The outcome is a point estimate; 95% confidence intervals can be obtained from the website www.predictcancer.org. Instructions for physician: Locate the patient’s age on the age axis. Draw a line straight upward to the points axis to determine how many points a patients receives for age. Repeat this process for the other axes, each time drawing straight upward to the points axis. Sum the points achieved for each predictor, and locate this sum on the “Total points” axis. Draw a line straight down to assess the survival probability for this patient. EQD₂ = equivalent dose in 2-Gy fractions; GTV = gross tumor volume; PET = positron emission tomography; WHO-PS = World Health Organization performance status.